Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2020 Apr 26;20(9):2458. doi: 10.3390/s20092458.
The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.
互联网数据中心 (IDC) 的安全性直接取决于其制冷系统的可靠性和稳定性。因此，本研究结合深度学习技术，提出了一种基于时间序列的创新混合故障诊断方法 (1D-CNN_GRU)，用于使用 1 维卷积神经网络 (1D-CNN) 和门控循环单元 (GRU) 的制冷系统。首先，应用 1D-CNN 自动提取传感器序列数据的局部抽象特征。其次，应用具有长短期记忆特性的 GRU 来捕获全局特征以及序列的动态信息。此外，引入批归一化和辍学以加速网络训练并解决过拟合问题。在 RP-1043 数据集上评估了所提出的混合算法的有效性和可靠性；根据实验结果，1D-CNN_GRU 与其他最先进的算法相比表现出最佳性能。此外，实验结果表明，1D-CNN_GRU 对小故障具有更高的识别率。
